KDD2019 Applied Data Science Track Session ADS9: E-commerce and Advertising

[まとめページへのリンク](https://qiita.com/kotaaaa/items/4f144d660d504cc091cf)

#Applied Data Science Track Session ADS9: E-commerce and Advertising Chair: Anne Kao (Boeing) 

## SMOILE: A Shopper Marketing Optimization and Inverse Learning Engine
Abhilash Reddy Chenreddy (University of Illinois at Chicago); Parshan Pakiman (University of Illinois at Chicago); Selvaprabu Nadarajah (Information and Decision Sciences); Ranganathan Chandrasekaran (Information and Decision Sciences); Rick Abens (Foresight ROI, Inc.) 

##### shopper marketing 

pre-store-tactics
in-store-tactics

sales volumn after marketing 
Why shopper Marketing important 

- it is one of the fatsest-growing of marketing for consumer packaged goods 
- it explains 3% to 5% of the total lift
- it accounts for 3% to 13% of the total marketing budget
- practically, mining historical lift and SM tactics as well as designing SM campaigns are challenging problems

How Brands Design Marketing Campaigns?

Historical data 
planning for SM Tactics: sequential decision-making problem over a finite planning horizon 
business constraints
Future SM campaign 

planning for SM tactics 
How traditional Models work?

marketing/media-mix model is widely used for lift attribution 
they can become hard-to-estimate when various business constraints are active.

SMOILE Model
 
- planing marketing campaigns 
- data generation process

modeling lift 

##### related contribution 
 
- empirical optimization 
- data-driven optimization 

modeling lift 

- we model lift as the summation of two parametric functions encoding effect of SM and non-SM factors on the total lift via linear combination of features of SM and non-SM factors. 

lift attribution inverse learning and tactic planning optimization 

##### data

- frozen breakfast and wings 
- two major retailers in the US
- 11 and 9 SM tactics 

##### SMOILE: performance on test set
 
SMOILE 

- is an integrated framework that merges multiple sources of data to attribute shopper marketing lift and design marketing campaigns 
- leverafes the structure of data generation processto compute lift and design SM campains that are consistent with datageneretion process 
- leverages comsumer behavior to fit better models of lift avoiding spurious results 
- streamlines the implementation of mining lift and designing SM campaigns
- can be efficiently solved via commercial optimization solvers



## Two-Sided Fairness for Repeated Matchings in Two-Sided Markets: A Case Study of a Ride-Hailing Platform 
Tom Sühr (Max Planck Institute for Software Systems); Asia J. Biega (Microsoft Research); Meike Zehlike (Max Planck Institute for Software Systems); Krishna P. Gummadi (Max Planck Institute for Software Systems); Abhijnan Chakraborty (Max Planck Institute for Software Systems) 

we use two-sided platforms in our everyday life 

- e-commerce 
- multimedia streaming 
- ride-hailing 

our focus: ride-hailing platform
what about drivers platform?
concerns about drivers in the ride-hailing platform industry
potential for issues on the driver side 
potential for inequality 
inequality in our dataset
modeling a two-sided ride-hailing platform  
modeling utility for both sides

brief history of fair matching long lines of works on fairness in matching markets 

- school admissions 
- ...

fairness of repeated matching 

- amortized parity 
- amortized  proportionality 


methods od matching drivers & customers 

- nearest driver first (NDF)
- worst-off driver first (WDF)

How Fair are the One-Sided methods WDF and NDF?

- IN WDF, the inequality in driver income decrease 
- bu it results in lower average income of the drivers

our proposal: take two sides together

- optimizinfg for common inequality measures directly is practically infeasible 
- instead minimize the difference ...

how does our two-sided method perform?
does the waiting time for the passengers increase?


## Reserve Price Failure Rate Prediction with Header Bidding in Display Advertising 
Achir Kalra (Forbes Media LLC); Chong Wang (S&P Global); Cristian Borcea (New Jersey Institute of Technology); Yi Chen (New Jersey Institute of Technology) 

display advertising is a big business
real-time bidding 

- publisher <> ad exchange <> real-time bidding advertisers

impression revenue in second-price auction

<!--
goal: predit tje probability that a reserve price of an impression -->

data censorship problem 
problem definition & proposed solution 

solution: survival analysis model
parametric survival model 
pairwise interaction tensor factorization 
features: 

- user: ids store, os, browser, network bandwidth, and devices
- ad placement ad unit size and ad-position 
- page: URL, channel sections, if the page is trending pages
- contexts: hour of a day, and referrer URL

header bidding regularization 
negative log likelihood

data and implementation 
dataset: ~16M impression collected by Forbes

##### eval:

- Weibull distribution works best for the proposed model 
- The proposed model significantly outperforms the baselines 

eval on header bids Only 

conclusion 

- proposed parametric survival model to predict the failure rate if reserve price of on online display ad impression in on ad exchange auction 
- the model is augmented bu pairwise interaction tensor factorization and header bidding regularization 
- the experimental results show that the proposed models with the Weibull distribution significantly outperforms the comparison systems
- our model can be adopted by a majority of online publishers because they can collect similar data.



## The Identification and Estimation of Direct and Indirect Effects in A/B Tests through Causal Mediation Analysis 
Xuan Yin (Etsy, Inc.); Liangjie Hong (Etsy, Inc.) 

##### background: 
user engagements of different products can be causally dependent 

examples of online products: 

- organic search and promoted listing 

##### intro: 

- we see casual dependency from A/B test results

##### induced change: 
a change in one product would induce users to change their behaviors in other products 

the most popular KPI is ATE from A/B tests 
suppose the underlying causal mechanizu is like 
rec module, search, conversion 

##### Questions 

- does ATE on conversion truly measure the contribution of rec module change to the marketplace?
- Is ATE on conversion still a good KPI for rec module?
- shall we just ignore the induced reduction in user engagement of search?

##### Problem of Funnel analysis 

- too heuristic No foundation 
- ambiguous 
 - which place shall get the point  
- too narrow
 - shall rec module get any point? 

it may destroy the causal interpretation of experimental results 
it subsets the experimental results based on post-treatment criteria 
direct Indirect effect 
how about we split ATE to two parts: direct effect and indirect effect?
use direct effect on conversion as KPI

##### Introduction to potential Outcome Framework
causal identification 
assumoptions > causal effects 

##### identification in Rubin causal model the model behind A/B test 
identification of ATE 
strong ignorability and SUTVA > ATE

casual mediation analysis(CMA)
sequantial ignorability and SUTVA 
we cannot CMA Directly in A/B tests

- multiple unmeasured causually-dependent mediators in A/B tests break SI and invarialant ... 

##### what we do 

- the literature of CMA is only a starting point 
- we proposenew measures for direct and indirect effects 
- we work out the assumptions that leadto new measure 
- we do the estimation and hypothesis testing using real data 
- we prove that 

##### generalize CMA

 - generalized SI and LSEM > GADE and GACME

##### Take-aways

- user engagement of different products can be casually dependent
- the current popular KPI in A/B tests, ATE (on Conversion) is undesirable to evaluate product change 
- Tight attribution metric from funnel analysis is not causally interpretable
- GADE and GACME are better KPI for evaluation purposes 
- They can be identified and easily estimated and testes in practice 



## Personalized Purchase Prediction of Market Baskets with Wasserstein-Based Sequence Matching 
Mathias Kraus (ETH Zurich); Stefan Feuerriegel (ETH Zurich)


definition of market basket and purchase history 
this work aims to identify similar customers to predict future purchases 

use approach uses simple K-nearest search to find similar customers 
what is the distance between two purchase which we use for KNN?

##### calculated 3 step 

- product embedding + cosine similarity 
- wasserstein distance 
- Dynamic time warping 

1, cosine similarity 

- obtain a multi-dimensional vector fir each product 
- similar products are close to each other such as substitutes like red and white wine.

2, Wasserstein distance 

- the Wasserstein distance measures the distance between two sets of products 
- it measures the minimum amount of distance the embedded products 
- previously utilized in NLP as a distance measure between document

3, Dynamic time warping 

- we utilized a customized form of dynamic time warping to find similarity between (sub-)
- sequences of market baskets 
- DTW has been shown to be a powerful distance measure between time series

based on the nearest neighbors, we make the prediction of the next market basket


##### data

- simplified Instacart
- product-level Instacart 
- Ta-feng grocery dataset

##### conclusion 

- combination of dynamic time warping for subsequence matching and the Wasserstein